class_docstr: Layer that processes dropout with exponential decayed keep rate during
  training.
  
  :param initial_keep_rate: the initial keep rate of decaying dropout.
  :param decay_interval: the decay interval of decaying dropout.
  :param decay_rate: the decay rate of decaying dropout.
  :param noise_shape: a 1D integer tensor representing the shape of the
      binary dropout mask that will be multiplied with the input.
  :param seed: a python integer to use as random seed.
  :param kwargs: standard layer keyword arguments.
  
  Examples:
      >>> import matchzoo as mz
      >>> layer = mz.contrib.layers.DecayingDropoutLayer(
      ...     initial_keep_rate=1.0,
      ...     decay_interval=10000,
      ...     decay_rate=0.977,
      ... )
      >>> num_batch, num_dim =5, 10
      >>> layer.build([num_batch, num_dim])
docstr: Build the layer.
  
  :param input_shape: the shape of the input tensor,
      for DecayingDropoutLayer we need one input tensor.
docstr_tok: ["Build","the","layer",".",":","param","input_shape",":","the","shape","of","the","input","tensor",",","for","DecayingDropoutLayer","we","need","one","input","tensor","."]
code: def build(self, input_shape):
      """
      Build the layer.
  
      :param input_shape: the shape of the input tensor,
          for DecayingDropoutLayer we need one input tensor.
      """
  
      self._iterations = self.add_weight(
          name="iterations",
          shape=(1,),
          dtype=K.floatx(),
          initializer="zeros",
          trainable=False,
      )
      super(DecayingDropoutLayer, self).build(input_shape)
  
file_path: MatchZoo/matchzoo/contrib/layers/decaying_dropout_layer.py
imports: import tensorflow
  from keras import backend
  from keras.engine import Layer
label_desc: Use image node_id 3 for calling the DecayingDropoutLayer obj's underlying member method code with example usage: obj.build(input_shape) without return types